Parameter Study for Differential Evolution Using a Power Allocation Problem Including Interference Cancellation

Zielinski, K.; Weitkemper, Petra; Laur, R.; Kammeyer, Karl‐Dirk

doi:10.1109/cec.2006.1688533

Cited by 97 publications

(57 citation statements)

References 9 publications

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“…This operation leads to an increase in the pool of potential trial vectors, thus reducing the risk of stagnation without increasing population size. Recently, a further parameter study with reference to real world problems has been carried out in Zielinskiand et al (2006).…”

Section: Introductionmentioning

confidence: 99%

Super-fit control adaptation in memetic differential evolution frameworks

2008

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This paper proposes the super-fit memetic differential evolution (SFMDE). This algorithm employs a differential evolution (DE) framework hybridized with three meta-heuristics, each having different roles and features. Particle Swarm Optimization assists the DE in the beginning of the optimization process by helping to generate a super-fit individual. The two other meta-heuristics are local searchers adaptively coordinated by means of an index measuring quality of the super-fit individual with respect to the rest of the population. The choice of the local searcher and its application is then executed by means of a probabilistic scheme which makes use of the generalized beta distribution. These two local searchers are the Nelder mead algorithm and the Rosenbrock Algorithm. The SFMDE has been tested on two engineering problems; the first application is the optimal control drive design for a direct current (DC) motor, the second is the design of a digital filter for image processing purposes. Numerical results show that the SFMDE is a flexible and promising approach which has a high performance standard in terms of both final solutions detected and convergence speed.A. Caponio

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Section: Introductionmentioning

confidence: 99%

Super-fit control adaptation in memetic differential evolution frameworks

2008

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“…In Price and Storn (1997), the settings F 2 [0.5,1] and CR 2 [0.8,1] are recommended. The empirical analysis reported in Zielinski et al (2006) shows that in many cases the setting of F 0.6 and CR 0.6 leads to results having better performance.…”

Section: Control Parameters Of Differential Evolutionmentioning

confidence: 95%

Bio-inspired Computation Algorithms

Duan

2013

Bio-Inspired Computation in Unmanned Aerial Vehicles

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Bio-inspired computation is the use of computers to model the living phenomena and simultaneously the study of life to improve the usage of computers. Swarm behaviors in animal groups such as bird flocks, bees, ants, fish schools, and sheep herds, as well as insects like mosquitoes, ants, and bees, often exhibit incredible abilities to solve complex problems that seem far beyond their capabilities. This chapter mainly focuses on the biological inspiration, principle, and implementation procedures of four popular bio-inspired computation algorithms including ant colony optimization (ACO), particle swarm optimization (PSO), artificial bee colony (ABC), and differential evolution (DE). Special emphasis has been laid on how the biological behavior can be transferred into a technical algorithm. Moreover, description of algorithms in more general terms and the most successful variants of these algorithms are provided. Finally, a brief introduction to other bio-inspired computation algorithms such as glowworm swarm optimization (GSM), bacteria foraging optimization (BFO), bat-inspired algorithm (BA) is presented. IntroductionBio-inspired computation, short for biologically inspired computation, is a way of developing computer systems by taking ideas from the biological world. It is a field of study that loosely knits together subfields related to the topics of connectionism, social behavior, and emergence. Briefly put, it is the use of computers to model the living phenomena and simultaneously the study of life to improve the usage of computers. Bio-inspired computation in an interdiscipline composed of many different fields, such as biology, computer science, physics, mathematics, and genetics. As we have known, biological systems have many advantages over computer systems, as they are able to solve much more complex problems beyond H. Duan and P. Li, Bio-inspired Computation in Unmanned Aerial Vehicles,

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“…Tests for real values in range [0, 1] lead to selection of 0.9 for CR and 0.9 for F. More explanation for the values of CR and F is presented in [10].…”

Section: Parameterizationmentioning

confidence: 99%

A Hybrid Cellular Genetic Algorithm for Multi-objective Crew Scheduling Problem

Jolai

Assadipour

2010

Lecture Notes in Computer Science

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Abstract. Crew scheduling is one of the important problems of the airline industry. This problem aims to cover a number of flights by crew members, such that all the flights are covered. In a robust scheduling the assignment should be so that the total cost, delays, and unbalanced utilization are minimized. As the problem is NP-hard and the objectives are in conflict with each other, a multiobjective meta-heuristic called CellDE, which is a hybrid cellular genetic algorithm, is implemented as the optimization method. The proposed algorithm provides the decision maker with a set of non-dominated or Pareto-optimal solutions, and enables them to choose the best one according to their preferences. A set of problems of different sizes is generated and solved using the proposed algorithm. Evaluating the performance of the proposed algorithm, three metrics are suggested, and the diversity and the convergence of the achieved Pareto front are appraised. Finally a comparison is made between CellDE and PAES, another meta-heuristic algorithm. The results show the superiority of CellDE.

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Parameter Study for Differential Evolution Using a Power Allocation Problem Including Interference Cancellation

Cited by 97 publications

References 9 publications

Super-fit control adaptation in memetic differential evolution frameworks

Super-fit control adaptation in memetic differential evolution frameworks

Bio-inspired Computation Algorithms

A Hybrid Cellular Genetic Algorithm for Multi-objective Crew Scheduling Problem

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